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linedance-app/venv/lib/python3.12/site-packages/numba/tests/test_blackscholes.py

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+import math
+
+import numpy as np
+
+import unittest
+from numba import njit
+from numba.extending import register_jitable
+from numba.tests.support import TestCase
+
+
+RISKFREE = 0.02
+VOLATILITY = 0.30
+
+
+A1 = 0.31938153
+A2 = -0.356563782
+A3 = 1.781477937
+A4 = -1.821255978
+A5 = 1.330274429
+RSQRT2PI = 0.39894228040143267793994605993438
+
+
+@register_jitable
+def cnd_array(d):
+    K = 1.0 / (1.0 + 0.2316419 * np.abs(d))
+    ret_val = (RSQRT2PI * np.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    return np.where(d > 0, 1.0 - ret_val, ret_val)
+
+
+@register_jitable
+def cnd(d):
+    K = 1.0 / (1.0 + 0.2316419 * math.fabs(d))
+    ret_val = (RSQRT2PI * math.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    if d > 0:
+        ret_val = 1.0 - ret_val
+    return ret_val
+
+
+@njit
+def blackscholes_arrayexpr(stockPrice, optionStrike, optionYears, Riskfree,
+                           Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    sqrtT = np.sqrt(T)
+    d1 = (np.log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT)
+    d2 = d1 - V * sqrtT
+    cndd1 = cnd_array(d1)
+    cndd2 = cnd_array(d2)
+
+    expRT = np.exp(- R * T)
+
+    callResult = (S * cndd1 - X * expRT * cndd2)
+    putResult = (X * expRT * (1.0 - cndd2) - S * (1.0 - cndd1))
+    return callResult, putResult
+
+
+@njit
+def blackscholes_scalar(callResult, putResult, stockPrice, optionStrike,
+                        optionYears, Riskfree, Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    for i in range(len(S)):
+        sqrtT = math.sqrt(T[i])
+        d1 = (math.log(S[i] / X[i]) + (R + 0.5 * V * V) * T[i]) / (V * sqrtT)
+        d2 = d1 - V * sqrtT
+        cndd1 = cnd(d1)
+        cndd2 = cnd(d2)
+
+        expRT = math.exp((-1. * R) * T[i])
+        callResult[i] = (S[i] * cndd1 - X[i] * expRT * cndd2)
+        putResult[i] = (X[i] * expRT * (1.0 - cndd2) - S[i] * (1.0 - cndd1))
+
+
+def randfloat(rand_var, low, high):
+    return (1.0 - rand_var) * low + rand_var * high
+
+
+class TestBlackScholes(TestCase):
+    def test_array_expr(self):
+        OPT_N = 400
+
+        stockPrice = randfloat(self.random.random_sample(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(self.random.random_sample(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(self.random.random_sample(OPT_N), 0.25, 10.0)
+
+        args = stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY
+
+        callResultGold, putResultGold = blackscholes_arrayexpr.py_func(*args)
+        callResultNumba, putResultNumba = blackscholes_arrayexpr(*args)
+
+        delta = np.abs(callResultGold - callResultNumba)
+        self.assertAlmostEqual(delta.max(), 0)
+
+    def test_scalar(self):
+        OPT_N = 400
+
+        callResultGold = np.zeros(OPT_N)
+        putResultGold = np.zeros(OPT_N)
+
+        callResultNumba = np.zeros(OPT_N)
+        putResultNumba = np.zeros(OPT_N)
+
+        stockPrice = randfloat(self.random.random_sample(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(self.random.random_sample(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(self.random.random_sample(OPT_N), 0.25, 10.0)
+
+        args = stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY
+
+        blackscholes_scalar.py_func(callResultGold, putResultGold, *args)
+        blackscholes_scalar(callResultNumba, putResultNumba, *args)
+
+        delta = np.abs(callResultGold - callResultNumba)
+        self.assertAlmostEqual(delta.max(), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()